Detecting Diseases Using Chest X-Ray Images and Pre-Trained Machine Learning Models

Chest X-ray imaging is an essential diagnostic tool for identifying various thoracic diseases such as pneumonia, tuberculosis, and COVID-19. The advent of deep learning, particularly pre-trained convolutional neural networks (CNNs), has significantly improved the automation and accuracy of disease detection in medical imaging. This paper investigates the use of several state-of-the-art pre-trained CNN architectures for detecting diseases from chest X-ray images. The models are fine-tuned and evaluated on publicly available datasets. Performance metrics such as accuracy, sensitivity, specificity, and F1-score are analyzed to demonstrate the effectiveness of transfer learning for medical diagnosis.